问题描述
我是python的新手,一直在尝试学习如何使用numpy和scipy.我有一个由LAS数据[x,y,z,强度,分类]组成的numpy数组.我已经创建了点的cKDTree,并使用 query_ball_point .我想找到由query_ball_point返回的邻居的z值的标准偏差,该值返回该点及其邻居的索引列表.
I am relatively new to python and have been trying to learn how to use numpy and scipy. I have a numpy array comprised of LAS data [x, y, z, intensity, classification]. I have created a cKDTree of points and have found nearest neighbors using query_ball_point. I would like to find standard deviation of the z values for the neighbors returned by query_ball_point, which returns a list of indices for the point and its neighbors.
是否有一种方法可以过滤filtered__rows以创建仅包含其索引在query_ball_point返回的列表中的点的数组?请参阅下面的代码.我可以将值附加到列表中并从中计算std dev,但我认为使用numpy在单轴上计算std dev会更容易.预先感谢.
Is there a way to filter filtered__rows to create an array of only points whose index is in the list returned by query_ball_point? See code below. I can append the values to a list and calculate std dev from that, but I think it would be easier to use numpy to calculate std dev on a single axis. Thanks in advance.
# Import modules
from liblas import file
import numpy as np
import scipy.spatial
if __name__=="__main__":
'''Read LAS file and create an array to hold X, Y, Z values'''
# Get file
las_file = r"E:\Testing\kd-tree_testing\LE_K20_clipped.las"
# Read file
f = file.File(las_file, mode='r')
# Get number of points from header
num_points = int(f.__len__())
# Create empty numpy array
PointsXYZIC = np.empty(shape=(num_points, 5))
# Load all LAS points into numpy array
counter = 0
for p in f:
newrow = [p.x, p.y, p.z, p.intensity, p.classification]
PointsXYZIC[counter] = newrow
counter += 1
'''Filter array to include classes 1 and 2'''
# the values to filter against
unclassified = 1
ground = 2
# Create an array of booleans
filter_array = np.any([PointsXYZIC[:, 4] == 1, PointsXYZIC[:, 4] == 2], axis=0)
# Use the booleans to index the original array
filtered_rows = PointsXYZIC[filter_array]
'''Create a KD tree structure and segment the point cloud'''
tree = scipy.spatial.cKDTree(filtered_rows, leafsize=10)
'''For each point in the point cloud use the KD tree to identify nearest neighbors,
with a K radius'''
k = 5 #meters
for pntIndex in range(len(filtered_rows)):
neighbor_list = tree.query_ball_point(filtered_rows[pntIndex], k)
zList = []
for neighbor in neighbor_list:
neighbor_z = filtered_rows[neighbor, 2]
zList.append(neighbor_z)
推荐答案
ummmm很难说出所要询问的内容(那是一堵墙)
ummmm Its hard to tell whats being asked (thats quite the wall of text)
filter_indices = [1,3,5]
print numpy.array([11,13,155,22,0xff,32,56,88])[filter_indices]
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